{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/admin/Dropbox/TAMU/Diss./Theory/Choice_set/RnR/Dataverse/FPA_main.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 1 Jan 2021, 23:41:10
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. ***     Data prep       *********************
. 
. ///     Create dependent variable: total options in choice-set
> ///     Since each policy is mesaured with a binary indicator, I add all 7 policy options
> ///     and create a variable for the total number of policies in a respondent's choice-set.
>  
. gen set_all=p1+p2+p3+p4+p5+p6+p7
{txt}
{com}. 
. /// Create variables for treatments in reduced sample (no control conditions)
> gen cas=.
{txt}(1036 missing values generated)

{com}. replace cas=0 if casualties==1
{txt}(425 real changes made)

{com}. replace cas=1 if casualties==2
{txt}(398 real changes made)

{com}. 
. gen oth=.
{txt}(1036 missing values generated)

{com}. replace oth=0 if other==1
{txt}(416 real changes made)

{com}. replace oth=1 if other==2
{txt}(407 real changes made)

{com}. 
. ********************************
. *********       Models  ************
. ********************************
. 
. *** Analysis 1: ANOVA models for choice-set size
. 
. /// Model 1: ANOVA, only treatments
> anova set_all horizon casualties other

                           {txt}Number of obs ={res}    1036     {txt}R-squared     ={res}  0.0571
                           {txt}Root MSE      ={res} 1.47887     {txt}Adj R-squared ={res}  0.0535

                  {txt}Source {c |}  Partial SS    df       MS           F     Prob > F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 136.580215     4  34.1450537      15.61     0.0000
                         {txt}{c |}
                 horizon {c |} {res}  6.1610346     1   6.1610346       2.82     0.0936
{txt}              casualties {c |} {res} 92.1670292     2  46.0835146      21.07     0.0000
{txt}                   other {c |} {res}  2.7008641     1   2.7008641       1.23     0.2667
                         {txt}{c |}
                Residual {c |} {res} 2254.84836  1031  2.18704981   
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 2391.42857  1035  2.31055901   
{txt}
{com}. 
. /// Contrasts: compute means per conditions and tests differences
> tukeyhsd horizon

{txt}Tukey HSD pairwise comparisons for variable horizon
studentized range critical value(.05, 2, 1031) = 2.7750654
uses harmonic mean sample size =  517.969

                                       mean 
grp vs grp       group means           dif    HSD-test
-------------------------------------------------------
{res}  1 vs   2     3.2101     3.3602      0.1500   2.3090{txt} 

Note: the levels of horizon have been recoded.

{com}. pwmean set_all, over(horizon) mcompare(tukey) effects

{txt}Pairwise comparisons of means with equal variances

{txt}{p2colset 1 14 16 2}{...}
{p2col:over}:{space 1}{res:horizon}{p_end}
{p2colreset}{...}

{res}{txt}{p 0 6 2}note: option tukey ignored since there is only one comparison{p_end}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 37}      Una{col 46}djusted{col 54}          Una{col 67}djusted
{col 1}     set_all{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}horizon {c |}
{space 5}1 vs 0  {c |}{col 14}{res}{space 2} .1500365{col 26}{space 2} .0943847{col 37}{space 1}    1.59{col 46}{space 3}0.112{col 54}{space 4}-.0351708{col 67}{space 3} .3352439
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. anova set_all horizon casualties other

                           {txt}Number of obs ={res}    1036     {txt}R-squared     ={res}  0.0571
                           {txt}Root MSE      ={res} 1.47887     {txt}Adj R-squared ={res}  0.0535

                  {txt}Source {c |}  Partial SS    df       MS           F     Prob > F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 136.580215     4  34.1450537      15.61     0.0000
                         {txt}{c |}
                 horizon {c |} {res}  6.1610346     1   6.1610346       2.82     0.0936
{txt}              casualties {c |} {res} 92.1670292     2  46.0835146      21.07     0.0000
{txt}                   other {c |} {res}  2.7008641     1   2.7008641       1.23     0.2667
                         {txt}{c |}
                Residual {c |} {res} 2254.84836  1031  2.18704981   
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 2391.42857  1035  2.31055901   
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. tukeyhsd other

{txt}Tukey HSD pairwise comparisons for variable other
studentized range critical value(.05, 3, 1031) = 3.3193271
uses harmonic mean sample size =  313.949

                                       mean 
grp vs grp       group means           dif    HSD-test
-------------------------------------------------------
{res}  1 vs   2     3.9765     3.0529      0.9236  11.0663{txt}*
{res}  1 vs   3     3.9765     3.1622      0.8144   9.7571{txt}*
{res}  2 vs   3     3.0529     3.1622      0.1093   1.3093{txt} 

Note: the levels of other have been recoded.

{com}. pwmean set_all, over(other) mcompare(tukey) effects

{txt}Pairwise comparisons of means with equal variances

{txt}{p2colset 1 14 16 2}{...}
{p2col:over}:{space 1}{res:other}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 13}
{col 14}{c |}    Number of
{col 14}{c |}  Comparisons
{hline 13}{c +}{hline 13}
{space 7}other {c |}{col 14}{res}{space 1}           3
{txt}{hline 13}{c BT}{hline 13}

{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 37}        T{col 46}ukey{col 54}            T{col 67}ukey
{col 1}     set_all{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}other {c |}
{space 5}1 vs 0  {c |}{col 14}{res}{space 2}-.9236412{col 26}{space 2} .1246495{col 37}{space 1}   -7.41{col 46}{space 3}0.000{col 54}{space 4}-1.216205{col 67}{space 3}-.6310776
{txt}{space 5}2 vs 0  {c |}{col 14}{res}{space 2}-.8143637{col 26}{space 2} .1251153{col 37}{space 1}   -6.51{col 46}{space 3}0.000{col 54}{space 4}-1.108021{col 67}{space 3}-.5207067
{txt}{space 5}2 vs 1  {c |}{col 14}{res}{space 2} .1092775{col 26}{space 2} .1031473{col 37}{space 1}    1.06{col 46}{space 3}0.540{col 54}{space 4}-.1328184{col 67}{space 3} .3513735
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. anova set_all horizon casualties other

                           {txt}Number of obs ={res}    1036     {txt}R-squared     ={res}  0.0571
                           {txt}Root MSE      ={res} 1.47887     {txt}Adj R-squared ={res}  0.0535

                  {txt}Source {c |}  Partial SS    df       MS           F     Prob > F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 136.580215     4  34.1450537      15.61     0.0000
                         {txt}{c |}
                 horizon {c |} {res}  6.1610346     1   6.1610346       2.82     0.0936
{txt}              casualties {c |} {res} 92.1670292     2  46.0835146      21.07     0.0000
{txt}                   other {c |} {res}  2.7008641     1   2.7008641       1.23     0.2667
                         {txt}{c |}
                Residual {c |} {res} 2254.84836  1031  2.18704981   
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 2391.42857  1035  2.31055901   
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. tukeyhsd casualties

{txt}Tukey HSD pairwise comparisons for variable casualties
studentized range critical value(.05, 3, 1031) = 3.3193271
uses harmonic mean sample size =  313.796

                                       mean 
grp vs grp       group means           dif    HSD-test
-------------------------------------------------------
{res}  1 vs   2     3.9765     3.1035      0.8730  10.4570{txt}*
{res}  1 vs   3     3.9765     3.1106      0.8660  10.3729{txt}*
{res}  2 vs   3     3.1035     3.1106      0.0070   0.0841{txt} 

Note: the levels of casualties have been recoded.

{com}. pwmean set_all, over(casualties) mcompare(tukey) effects

{txt}Pairwise comparisons of means with equal variances

{txt}{p2colset 1 14 16 2}{...}
{p2col:over}:{space 1}{res:casualties}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 13}
{col 14}{c |}    Number of
{col 14}{c |}  Comparisons
{hline 13}{c +}{hline 13}
{space 2}casualties {c |}{col 14}{res}{space 1}           3
{txt}{hline 13}{c BT}{hline 13}

{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 37}        T{col 46}ukey{col 54}            T{col 67}ukey
{col 1}     set_all{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}casualties {c |}
{space 5}1 vs 0  {c |}{col 14}{res}{space 2}-.8729964{col 26}{space 2} .1242689{col 37}{space 1}   -7.03{col 46}{space 3}0.000{col 54}{space 4}-1.164667{col 67}{space 3} -.581326
{txt}{space 5}2 vs 0  {c |}{col 14}{res}{space 2}-.8659731{col 26}{space 2} .1256683{col 37}{space 1}   -6.89{col 46}{space 3}0.000{col 54}{space 4}-1.160928{col 67}{space 3}-.5710182
{txt}{space 5}2 vs 1  {c |}{col 14}{res}{space 2} .0070234{col 26}{space 2} .1032525{col 37}{space 1}    0.07{col 46}{space 3}0.997{col 54}{space 4}-.2353195{col 67}{space 3} .2493662
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Model 2: ANOVA, full model
> anova set_all horizon casualties other gender age party fp_know edu_cat 

                           {txt}Number of obs ={res}    1034     {txt}R-squared     ={res}  0.1938
                           {txt}Root MSE      ={res} 1.41477     {txt}Adj R-squared ={res}  0.1298

                  {txt}Source {c |}  Partial SS    df       MS           F     Prob > F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 460.471402    76  6.05883424       3.03     0.0000
                         {txt}{c |}
                 horizon {c |} {res} 5.57371276     1  5.57371276       2.78     0.0955
{txt}              casualties {c |} {res} 72.5264114     2  36.2632057      18.12     0.0000
{txt}                   other {c |} {res} 1.86760256     1  1.86760256       0.93     0.3343
{txt}                  gender {c |} {res} .148743738     1  .148743738       0.07     0.7852
{txt}                     age {c |} {res} 146.298063    58   2.5223804       1.26     0.0957
{txt}                   party {c |} {res} 78.2605595     6  13.0434266       6.52     0.0000
{txt}                 fp_know {c |} {res} 35.3741385     3  11.7913795       5.89     0.0006
{txt}                 edu_cat {c |} {res} 23.7897149     4  5.94742872       2.97     0.0187
                         {txt}{c |}
                Residual {c |} {res} 1915.50249   957     2.00157   
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 2375.97389  1033  2.30007153   
{txt}
{com}. 
. /// Contrasts: compute means per conditions and tests differences
> tukeyhsd horizon

{txt}Tukey HSD pairwise comparisons for variable horizon
studentized range critical value(.05, 2, 957) = 2.7753177
uses harmonic mean sample size =  517.969

                                       mean 
grp vs grp       group means           dif    HSD-test
-------------------------------------------------------
{res}  1 vs   2     3.2101     3.3602      0.1500   2.4136{txt} 

Note: the levels of horizon have been recoded.

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Create age group variables based on sample distribution
> /// Then, compute mean size of choice-set by group
> sum age, detail

                             {txt}Age
{hline 61}
      Percentiles      Smallest
 1%    {res}       20             18
{txt} 5%    {res}       23             18
{txt}10%    {res}       25             18       {txt}Obs         {res}       1036
{txt}25%    {res}       29             19       {txt}Sum of Wgt. {res}       1036

{txt}50%    {res}       35                      {txt}Mean          {res}  38.2027
                        {txt}Largest       Std. Dev.     {res} 12.45605
{txt}75%    {res}       46             74
{txt}90%    {res}       57             77       {txt}Variance      {res} 155.1531
{txt}95%    {res}       63             78       {txt}Skewness      {res} .8479546
{txt}99%    {res}       70             79       {txt}Kurtosis      {res} 2.953738
{txt}
{com}. 
. gen age_cat=.
{txt}(1036 missing values generated)

{com}. replace age_cat=1 if age<30
{txt}(288 real changes made)

{com}. replace age_cat=2 if age>29 & age<46
{txt}(481 real changes made)

{com}. replace age_cat=3 if age>45 & age<57
{txt}(146 real changes made)

{com}. replace age_cat=4 if age>56
{txt}(121 real changes made)

{com}. 
. /// Mean choice-set size for different age groups (ANOVA model 2)
> tabstat set_all, by(age_cat) statistics(mean sd semean)

{txt}Summary for variables: set_all
{col 6}by categories of: age_cat 

{ralign 8:age_cat} {...}
{c |}      mean        sd  se(mean)
{hline 9}{c +}{hline 30}
{ralign 8:1} {...}
{c |}{...}
 {res} 3.385417  1.450962  .0854988
{txt}{ralign 8:2} {...}
{c |}{...}
 {res} 3.351351  1.551627  .0707481
{txt}{ralign 8:3} {...}
{c |}{...}
 {res}  3.19863  1.574013  .1302662
{txt}{ralign 8:4} {...}
{c |}{...}
 {res} 2.892562   1.43644  .1305855
{txt}{hline 9}{c +}{hline 30}
{ralign 8:Total} {...}
{c |}{...}
 {res} 3.285714  1.520052  .0472257
{txt}{hline 9}{c BT}{hline 30}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Create varibles for control conditions only (reduced sample analysis)
> gen horz=.
{txt}(1036 missing values generated)

{com}. replace horz=horizon if casualties==0
{txt}(213 real changes made)

{com}. 
. /// Create varible for control conditions and choice-set size 
> gen horz2=horz if set_all>1
{txt}(834 missing values generated)

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Run univariate OLS regression model with reduced sample
> /// Compute mean values for both conditions using marginal effects (fit to figure 4 in main text)
> /// Model results are diplayed in appendix file (table 3, model 3)
> reg set_all horz2 gender party age fp_know edu_cat 

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     202
{txt}{hline 13}{char +}{hline 30}           F(  6,   195) ={res}    2.22
    {txt}   Model {char |} {res} 27.4182806     6  4.56971344           {txt}Prob > F      = {res} 0.0432
    {txt}Residual {char |} {res} 402.126274   195  2.06218602           {txt}R-squared     = {res} 0.0638
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.0350
    {txt}   Total {char |} {res} 429.544554   201  2.13703758           {txt}Root MSE      = {res}  1.436

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     set_all{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}horz2 {c |}{col 14}{res}{space 2} .3419019{col 26}{space 2} .2045569{col 37}{space 1}    1.67{col 46}{space 3}0.096{col 54}{space 4}-.0615261{col 67}{space 3} .7453298
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .1378316{col 26}{space 2} .2053874{col 37}{space 1}    0.67{col 46}{space 3}0.503{col 54}{space 4}-.2672344{col 67}{space 3} .5428975
{txt}{space 7}party {c |}{col 14}{res}{space 2} .0854974{col 26}{space 2} .0492709{col 37}{space 1}    1.74{col 46}{space 3}0.084{col 54}{space 4} -.011675{col 67}{space 3} .1826698
{txt}{space 9}age {c |}{col 14}{res}{space 2} -.008348{col 26}{space 2} .0082652{col 37}{space 1}   -1.01{col 46}{space 3}0.314{col 54}{space 4}-.0246487{col 67}{space 3} .0079526
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .2597339{col 26}{space 2} .1188311{col 37}{space 1}    2.19{col 46}{space 3}0.030{col 54}{space 4} .0253748{col 67}{space 3} .4940931
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .0149653{col 26}{space 2} .1159734{col 37}{space 1}    0.13{col 46}{space 3}0.897{col 54}{space 4} -.213758{col 67}{space 3} .2436886
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  3.18633{col 26}{space 2} .6287473{col 37}{space 1}    5.07{col 46}{space 3}0.000{col 54}{space 4} 1.946312{col 67}{space 3} 4.426348
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. margins, at(horz2=(0 1))
{res}
{txt}Predictive margins{col 51}Number of obs{col 67}= {res}       202
{txt}Model VCE{col 14}: {res}OLS

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:horz2}{space 11}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:horz2}{space 11}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 3.977564{col 26}{space 2} .1437697{col 37}{space 1}   27.67{col 46}{space 3}0.000{col 54}{space 4} 3.694021{col 67}{space 3} 4.261107
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 4.319466{col 26}{space 2} .1437697{col 37}{space 1}   30.04{col 46}{space 3}0.000{col 54}{space 4} 4.035923{col 67}{space 3} 4.603009
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. *** Analysis 2: choice-set composition with probit interaction models
. 
. /// Probit interaction models (reduced sample, no control conditions)
> ///     Compute marginal effects for all conditions
> /// Results of these models are displayed in appendix file (table 3, models 1&2)
> probit p1 i.horizon i.oth i.cas i.horizon##i.oth gender age party fp_know edu_cat

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -480.1729}  
Iteration 1:{space 3}log likelihood = {res: -342.2914}  
Iteration 2:{space 3}log likelihood = {res:-337.90064}  
Iteration 3:{space 3}log likelihood = {res:-337.88262}  
Iteration 4:{space 3}log likelihood = {res:-337.88262}  
{res}
{txt}Probit regression{col 51}Number of obs{col 67}= {res}       821
{txt}{col 51}LR chi2({res}9{txt}){col 67}= {res}    284.58
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-337.88262{txt}{col 51}Pseudo R2{col 67}= {res}    0.2963

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          p1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.horizon {c |}{col 14}{res}{space 2} .1232022{col 26}{space 2} .2225798{col 37}{space 1}    0.55{col 46}{space 3}0.580{col 54}{space 4}-.3130461{col 67}{space 3} .5594505
{txt}{space 7}1.oth {c |}{col 14}{res}{space 2}-1.977937{col 26}{space 2}     .177{col 37}{space 1}  -11.17{col 46}{space 3}0.000{col 54}{space 4} -2.32485{col 67}{space 3}-1.631023
{txt}{space 7}1.cas {c |}{col 14}{res}{space 2} -.096351{col 26}{space 2} .1105881{col 37}{space 1}   -0.87{col 46}{space 3}0.384{col 54}{space 4}-.3130996{col 67}{space 3} .1203977
{txt}{space 12} {c |}
{space 1}horizon#oth {c |}
{space 8}1 1  {c |}{col 14}{res}{space 2} .4292367{col 26}{space 2} .2564686{col 37}{space 1}    1.67{col 46}{space 3}0.094{col 54}{space 4}-.0734326{col 67}{space 3}  .931906
{txt}{space 12} {c |}
{space 6}gender {c |}{col 14}{res}{space 2} .1152895{col 26}{space 2} .1152772{col 37}{space 1}    1.00{col 46}{space 3}0.317{col 54}{space 4}-.1106497{col 67}{space 3} .3412288
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0073238{col 26}{space 2}  .004559{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4}-.0016117{col 67}{space 3} .0162593
{txt}{space 7}party {c |}{col 14}{res}{space 2} .0867894{col 26}{space 2} .0279931{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4} .0319239{col 67}{space 3}  .141655
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2}  -.05549{col 26}{space 2} .0710893{col 37}{space 1}   -0.78{col 46}{space 3}0.435{col 54}{space 4}-.1948224{col 67}{space 3} .0838425
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .0441662{col 26}{space 2}  .068398{col 37}{space 1}    0.65{col 46}{space 3}0.518{col 54}{space 4}-.0898914{col 67}{space 3} .1782239
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.059225{col 26}{space 2} .3737589{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .3266713{col 67}{space 3} 1.791779
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. margins, at(oth=(0 1) horizon=(0 1))
{res}
{txt}Predictive margins{col 51}Number of obs{col 67}= {res}       821
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(p1), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:oth}{space 13}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:oth}{space 13}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:oth}{space 13}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:oth}{space 13}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .9511725{col 26}{space 2} .0149737{col 37}{space 1}   63.52{col 46}{space 3}0.000{col 54}{space 4} .9218246{col 67}{space 3} .9805205
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3905505{col 26}{space 2} .0335238{col 37}{space 1}   11.65{col 46}{space 3}0.000{col 54}{space 4}  .324845{col 67}{space 3} .4562559
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .9622058{col 26}{space 2} .0131111{col 37}{space 1}   73.39{col 46}{space 3}0.000{col 54}{space 4} .9365084{col 67}{space 3} .9879032
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .6034068{col 26}{space 2} .0345063{col 37}{space 1}   17.49{col 46}{space 3}0.000{col 54}{space 4} .5357757{col 67}{space 3} .6710379
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. probit p2 i.horizon i.oth i.cas i.horizon##i.oth gender age party fp_know edu_cat

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-517.83484}  
Iteration 1:{space 3}log likelihood = {res:-411.41128}  
Iteration 2:{space 3}log likelihood = {res:-410.14109}  
Iteration 3:{space 3}log likelihood = {res:-410.13996}  
Iteration 4:{space 3}log likelihood = {res:-410.13996}  
{res}
{txt}Probit regression{col 51}Number of obs{col 67}= {res}       821
{txt}{col 51}LR chi2({res}9{txt}){col 67}= {res}    215.39
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-410.13996{txt}{col 51}Pseudo R2{col 67}= {res}    0.2080

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          p2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.horizon {c |}{col 14}{res}{space 2} .2278596{col 26}{space 2} .1251036{col 37}{space 1}    1.82{col 46}{space 3}0.069{col 54}{space 4}-.0173391{col 67}{space 3} .4730582
{txt}{space 7}1.oth {c |}{col 14}{res}{space 2}  1.48123{col 26}{space 2} .1480354{col 37}{space 1}   10.01{col 46}{space 3}0.000{col 54}{space 4} 1.191086{col 67}{space 3} 1.771374
{txt}{space 7}1.cas {c |}{col 14}{res}{space 2}-.1861998{col 26}{space 2} .1010392{col 37}{space 1}   -1.84{col 46}{space 3}0.065{col 54}{space 4}-.3842329{col 67}{space 3} .0118333
{txt}{space 12} {c |}
{space 1}horizon#oth {c |}
{space 8}1 1  {c |}{col 14}{res}{space 2}-.0777677{col 26}{space 2} .2140127{col 37}{space 1}   -0.36{col 46}{space 3}0.716{col 54}{space 4}-.4972249{col 67}{space 3} .3416895
{txt}{space 12} {c |}
{space 6}gender {c |}{col 14}{res}{space 2}-.0509545{col 26}{space 2} .1038024{col 37}{space 1}   -0.49{col 46}{space 3}0.624{col 54}{space 4}-.2544034{col 67}{space 3} .1524945
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0024747{col 26}{space 2} .0040584{col 37}{space 1}   -0.61{col 46}{space 3}0.542{col 54}{space 4}-.0104289{col 67}{space 3} .0054795
{txt}{space 7}party {c |}{col 14}{res}{space 2}-.0333568{col 26}{space 2} .0257781{col 37}{space 1}   -1.29{col 46}{space 3}0.196{col 54}{space 4}-.0838809{col 67}{space 3} .0171674
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2}-.0214695{col 26}{space 2} .0636583{col 37}{space 1}   -0.34{col 46}{space 3}0.736{col 54}{space 4}-.1462375{col 67}{space 3} .1032986
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .1229644{col 26}{space 2} .0613047{col 37}{space 1}    2.01{col 46}{space 3}0.045{col 54}{space 4} .0028094{col 67}{space 3} .2431195
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3063438{col 26}{space 2} .3123112{col 37}{space 1}   -0.98{col 46}{space 3}0.327{col 54}{space 4}-.9184626{col 67}{space 3}  .305775
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. margins, at(oth=(0 1) horizon=(0 1))
{res}
{txt}Predictive margins{col 51}Number of obs{col 67}= {res}       821
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(p2), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:oth}{space 13}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:oth}{space 13}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:oth}{space 13}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:oth}{space 13}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}   .40658{col 26}{space 2} .0346713{col 37}{space 1}   11.73{col 46}{space 3}0.000{col 54}{space 4} .3386255{col 67}{space 3} .4745345
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .8898959{col 26}{space 2} .0216462{col 37}{space 1}   41.11{col 46}{space 3}0.000{col 54}{space 4} .8474702{col 67}{space 3} .9323217
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4954687{col 26}{space 2}  .033816{col 37}{space 1}   14.65{col 46}{space 3}0.000{col 54}{space 4} .4291905{col 67}{space 3} .5617469
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .9152974{col 26}{space 2} .0196663{col 37}{space 1}   46.54{col 46}{space 3}0.000{col 54}{space 4} .8767521{col 67}{space 3} .9538427
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. *** Analysis 3: policy selection (phase 2 of experiment)
. 
. /// Selected policy: Multinomial regression model
> /// Model results are displayed in main text (table 1: include only models 2-4)
> mlogit pol_select horizon other casualties set_all ///
> gender age party fp_know edu_cat, b(1)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1024.3835}  
Iteration 1:{space 3}log likelihood = {res:-714.75982}  
Iteration 2:{space 3}log likelihood = {res:-674.82014}  
Iteration 3:{space 3}log likelihood = {res:-673.46077}  
Iteration 4:{space 3}log likelihood = {res:-673.45474}  
Iteration 5:{space 3}log likelihood = {res:-673.45474}  
{res}
{txt}Multinomial logistic regression{col 51}Number of obs{col 67}= {res}      1020
{txt}{col 51}LR chi2({res}54{txt}){col 67}= {res}    701.86
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-673.45474{txt}{col 51}Pseudo R2{col 67}= {res}    0.3426

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  pol_select{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}1           {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2}-.4829312{col 26}{space 2} .2022029{col 37}{space 1}   -2.39{col 46}{space 3}0.017{col 54}{space 4}-.8792417{col 67}{space 3}-.0866207
{txt}{space 7}other {c |}{col 14}{res}{space 2} 3.696906{col 26}{space 2} .2170267{col 37}{space 1}   17.03{col 46}{space 3}0.000{col 54}{space 4} 3.271541{col 67}{space 3}  4.12227
{txt}{space 2}casualties {c |}{col 14}{res}{space 2}-.8017417{col 26}{space 2} .1979314{col 37}{space 1}   -4.05{col 46}{space 3}0.000{col 54}{space 4} -1.18968{col 67}{space 3}-.4138032
{txt}{space 5}set_all {c |}{col 14}{res}{space 2} .2184247{col 26}{space 2}  .075148{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0711373{col 67}{space 3} .3657122
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .2055796{col 26}{space 2} .2065105{col 37}{space 1}    1.00{col 46}{space 3}0.319{col 54}{space 4}-.1991736{col 67}{space 3} .6103327
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0241361{col 26}{space 2} .0082402{col 37}{space 1}   -2.93{col 46}{space 3}0.003{col 54}{space 4}-.0402867{col 67}{space 3}-.0079855
{txt}{space 7}party {c |}{col 14}{res}{space 2}-.0122272{col 26}{space 2} .0509343{col 37}{space 1}   -0.24{col 46}{space 3}0.810{col 54}{space 4}-.1120566{col 67}{space 3} .0876022
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .1943762{col 26}{space 2} .1297736{col 37}{space 1}    1.50{col 46}{space 3}0.134{col 54}{space 4}-.0599752{col 67}{space 3} .4487277
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2}   .20385{col 26}{space 2} .1249348{col 37}{space 1}    1.63{col 46}{space 3}0.103{col 54}{space 4}-.0410177{col 67}{space 3} .4487177
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-5.591204{col 26}{space 2} .7555506{col 37}{space 1}   -7.40{col 46}{space 3}0.000{col 54}{space 4}-7.072056{col 67}{space 3}-4.110352
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2}-.8823256{col 26}{space 2} .4148236{col 37}{space 1}   -2.13{col 46}{space 3}0.033{col 54}{space 4}-1.695365{col 67}{space 3}-.0692864
{txt}{space 7}other {c |}{col 14}{res}{space 2} 2.730537{col 26}{space 2} .4005784{col 37}{space 1}    6.82{col 46}{space 3}0.000{col 54}{space 4} 1.945418{col 67}{space 3} 3.515656
{txt}{space 2}casualties {c |}{col 14}{res}{space 2}-.5682556{col 26}{space 2} .3777923{col 37}{space 1}   -1.50{col 46}{space 3}0.133{col 54}{space 4}-1.308715{col 67}{space 3} .1722038
{txt}{space 5}set_all {c |}{col 14}{res}{space 2} .6094191{col 26}{space 2} .1421246{col 37}{space 1}    4.29{col 46}{space 3}0.000{col 54}{space 4} .3308601{col 67}{space 3} .8879781
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.4320801{col 26}{space 2} .4338637{col 37}{space 1}   -1.00{col 46}{space 3}0.319{col 54}{space 4}-1.282437{col 67}{space 3} .4182771
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0917485{col 26}{space 2} .0268359{col 37}{space 1}   -3.42{col 46}{space 3}0.001{col 54}{space 4} -.144346{col 67}{space 3}-.0391511
{txt}{space 7}party {c |}{col 14}{res}{space 2}  .100589{col 26}{space 2} .1054367{col 37}{space 1}    0.95{col 46}{space 3}0.340{col 54}{space 4}-.1060631{col 67}{space 3} .3072411
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2}  .398631{col 26}{space 2} .2604926{col 37}{space 1}    1.53{col 46}{space 3}0.126{col 54}{space 4} -.111925{col 67}{space 3}  .909187
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .1208846{col 26}{space 2}  .280847{col 37}{space 1}    0.43{col 46}{space 3}0.667{col 54}{space 4}-.4295654{col 67}{space 3} .6713347
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-6.027349{col 26}{space 2} 1.457068{col 37}{space 1}   -4.14{col 46}{space 3}0.000{col 54}{space 4} -8.88315{col 67}{space 3}-3.171548
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}4            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2}-.9724512{col 26}{space 2} .5622312{col 37}{space 1}   -1.73{col 46}{space 3}0.084{col 54}{space 4}-2.074404{col 67}{space 3} .1295017
{txt}{space 7}other {c |}{col 14}{res}{space 2} 1.176471{col 26}{space 2} .5424496{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .1132898{col 67}{space 3} 2.239653
{txt}{space 2}casualties {c |}{col 14}{res}{space 2} -.104242{col 26}{space 2} .4865086{col 37}{space 1}   -0.21{col 46}{space 3}0.830{col 54}{space 4}-1.057781{col 67}{space 3} .8492972
{txt}{space 5}set_all {c |}{col 14}{res}{space 2} .4787219{col 26}{space 2} .1728186{col 37}{space 1}    2.77{col 46}{space 3}0.006{col 54}{space 4} .1400037{col 67}{space 3} .8174401
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.3814303{col 26}{space 2} .5474749{col 37}{space 1}   -0.70{col 46}{space 3}0.486{col 54}{space 4}-1.454461{col 67}{space 3} .6916007
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0137299{col 26}{space 2}  .022591{col 37}{space 1}   -0.61{col 46}{space 3}0.543{col 54}{space 4}-.0580074{col 67}{space 3} .0305475
{txt}{space 7}party {c |}{col 14}{res}{space 2} .0681721{col 26}{space 2} .1353303{col 37}{space 1}    0.50{col 46}{space 3}0.614{col 54}{space 4}-.1970704{col 67}{space 3} .3334145
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .0458402{col 26}{space 2} .3344099{col 37}{space 1}    0.14{col 46}{space 3}0.891{col 54}{space 4}-.6095912{col 67}{space 3} .7012717
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2}-.1262192{col 26}{space 2} .3283659{col 37}{space 1}   -0.38{col 46}{space 3}0.701{col 54}{space 4}-.7698046{col 67}{space 3} .5173662
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -5.30455{col 26}{space 2} 1.665221{col 37}{space 1}   -3.19{col 46}{space 3}0.001{col 54}{space 4}-8.568324{col 67}{space 3}-2.040776
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}5            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2}-.1011993{col 26}{space 2} .4914724{col 37}{space 1}   -0.21{col 46}{space 3}0.837{col 54}{space 4}-1.064467{col 67}{space 3} .8620688
{txt}{space 7}other {c |}{col 14}{res}{space 2} 1.362126{col 26}{space 2}  .489142{col 37}{space 1}    2.78{col 46}{space 3}0.005{col 54}{space 4} .4034252{col 67}{space 3} 2.320827
{txt}{space 2}casualties {c |}{col 14}{res}{space 2} -.604231{col 26}{space 2} .4689563{col 37}{space 1}   -1.29{col 46}{space 3}0.198{col 54}{space 4}-1.523368{col 67}{space 3} .3149065
{txt}{space 5}set_all {c |}{col 14}{res}{space 2} .3334768{col 26}{space 2} .1650882{col 37}{space 1}    2.02{col 46}{space 3}0.043{col 54}{space 4} .0099099{col 67}{space 3} .6570436
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-1.613825{col 26}{space 2} .6466308{col 37}{space 1}   -2.50{col 46}{space 3}0.013{col 54}{space 4}-2.881198{col 67}{space 3}-.3464517
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0162689{col 26}{space 2} .0208916{col 37}{space 1}   -0.78{col 46}{space 3}0.436{col 54}{space 4}-.0572157{col 67}{space 3} .0246778
{txt}{space 7}party {c |}{col 14}{res}{space 2}  .153387{col 26}{space 2} .1323497{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}-.1060137{col 67}{space 3} .4127877
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2}-.2091036{col 26}{space 2}  .311531{col 37}{space 1}   -0.67{col 46}{space 3}0.502{col 54}{space 4}-.8196931{col 67}{space 3} .4014859
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2}-.0508176{col 26}{space 2}  .295703{col 37}{space 1}   -0.17{col 46}{space 3}0.864{col 54}{space 4}-.6303848{col 67}{space 3} .5287496
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-4.112073{col 26}{space 2} 1.521138{col 37}{space 1}   -2.70{col 46}{space 3}0.007{col 54}{space 4}-7.093448{col 67}{space 3}-1.130698
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}6            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2}  .519962{col 26}{space 2} .7145863{col 37}{space 1}    0.73{col 46}{space 3}0.467{col 54}{space 4}-.8806013{col 67}{space 3} 1.920525
{txt}{space 7}other {c |}{col 14}{res}{space 2} 2.304925{col 26}{space 2}  .613312{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} 1.102855{col 67}{space 3} 3.506994
{txt}{space 2}casualties {c |}{col 14}{res}{space 2}-1.350552{col 26}{space 2} .6723034{col 37}{space 1}   -2.01{col 46}{space 3}0.045{col 54}{space 4}-2.668243{col 67}{space 3}-.0328619
{txt}{space 5}set_all {c |}{col 14}{res}{space 2} .3987269{col 26}{space 2} .2167915{col 37}{space 1}    1.84{col 46}{space 3}0.066{col 54}{space 4}-.0261767{col 67}{space 3} .8236305
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0163163{col 26}{space 2} .6869185{col 37}{space 1}   -0.02{col 46}{space 3}0.981{col 54}{space 4}-1.362652{col 67}{space 3} 1.330019
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0610594{col 26}{space 2} .0376141{col 37}{space 1}   -1.62{col 46}{space 3}0.105{col 54}{space 4}-.1347817{col 67}{space 3}  .012663
{txt}{space 7}party {c |}{col 14}{res}{space 2} .1835883{col 26}{space 2} .1686472{col 37}{space 1}    1.09{col 46}{space 3}0.276{col 54}{space 4}-.1469541{col 67}{space 3} .5141308
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .8527696{col 26}{space 2} .4452455{col 37}{space 1}    1.92{col 46}{space 3}0.055{col 54}{space 4}-.0198956{col 67}{space 3} 1.725435
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2}-.0753377{col 26}{space 2} .4632884{col 37}{space 1}   -0.16{col 46}{space 3}0.871{col 54}{space 4}-.9833663{col 67}{space 3} .8326909
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -7.53422{col 26}{space 2} 2.366239{col 37}{space 1}   -3.18{col 46}{space 3}0.001{col 54}{space 4}-12.17196{col 67}{space 3}-2.896477
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}7            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2} .3441368{col 26}{space 2} .6410314{col 37}{space 1}    0.54{col 46}{space 3}0.591{col 54}{space 4}-.9122616{col 67}{space 3} 1.600535
{txt}{space 7}other {c |}{col 14}{res}{space 2} 1.059144{col 26}{space 2} .6068258{col 37}{space 1}    1.75{col 46}{space 3}0.081{col 54}{space 4}-.1302124{col 67}{space 3} 2.248501
{txt}{space 2}casualties {c |}{col 14}{res}{space 2}-.8281985{col 26}{space 2} .5928134{col 37}{space 1}   -1.40{col 46}{space 3}0.162{col 54}{space 4}-1.990091{col 67}{space 3} .3336943
{txt}{space 5}set_all {c |}{col 14}{res}{space 2}-.3139653{col 26}{space 2} .2415821{col 37}{space 1}   -1.30{col 46}{space 3}0.194{col 54}{space 4}-.7874576{col 67}{space 3}  .159527
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0714093{col 26}{space 2} .6322718{col 37}{space 1}   -0.11{col 46}{space 3}0.910{col 54}{space 4}-1.310639{col 67}{space 3} 1.167821
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0223507{col 26}{space 2} .0263525{col 37}{space 1}   -0.85{col 46}{space 3}0.396{col 54}{space 4}-.0740007{col 67}{space 3} .0292993
{txt}{space 7}party {c |}{col 14}{res}{space 2} .3082036{col 26}{space 2} .1698118{col 37}{space 1}    1.81{col 46}{space 3}0.070{col 54}{space 4}-.0246215{col 67}{space 3} .6410286
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .1973757{col 26}{space 2} .3919026{col 37}{space 1}    0.50{col 46}{space 3}0.615{col 54}{space 4}-.5707393{col 67}{space 3} .9654907
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .1564659{col 26}{space 2} .3761779{col 37}{space 1}    0.42{col 46}{space 3}0.677{col 54}{space 4}-.5808292{col 67}{space 3}  .893761
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-4.851624{col 26}{space 2} 2.140794{col 37}{space 1}   -2.27{col 46}{space 3}0.023{col 54}{space 4}-9.047502{col 67}{space 3} -.655745
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. margins, at(other=(1 2) horizon=(0 1)) predict(outcome(2))
{res}
{txt}Predictive margins{col 51}Number of obs{col 67}= {res}      1020
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(pol_select==2), predict(outcome(2))}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:other}{space 11}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:other}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:other}{space 11}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:other}{space 11}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1488647{col 26}{space 2} .0221722{col 37}{space 1}    6.71{col 46}{space 3}0.000{col 54}{space 4} .1054079{col 67}{space 3} .1923215
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .7568293{col 26}{space 2} .0286823{col 37}{space 1}   26.39{col 46}{space 3}0.000{col 54}{space 4}  .700613{col 67}{space 3} .8130457
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1043086{col 26}{space 2}  .017675{col 37}{space 1}    5.90{col 46}{space 3}0.000{col 54}{space 4} .0696663{col 67}{space 3}  .138951
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .6988919{col 26}{space 2} .0314583{col 37}{space 1}   22.22{col 46}{space 3}0.000{col 54}{space 4} .6372347{col 67}{space 3} .7605491
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. *** Analysis 4: policy selection/preference reversal (phase 2 of experiment)
. *** The role of the choice-set size
. 
. /// Create binary indicator for seleting policy 1
> gen p1_sel=0
{txt}
{com}. replace p1_sel=1 if pol_select==1
{txt}(613 real changes made)

{com}. tab p1_sel

     {txt}p1_sel {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        423       40.83       40.83
{txt}          1 {c |}{res}        613       59.17      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,036      100.00
{txt}
{com}. 
. /// Probit regression model: also account for choice set size
> ///     Compute marginal effect of choice-set size 
> probit p1_sel horizon other casualties set_all gender age party fp_know edu_cat

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-699.15812}  
Iteration 1:{space 3}log likelihood = {res:-443.14899}  
Iteration 2:{space 3}log likelihood = {res:-440.59258}  
Iteration 3:{space 3}log likelihood = {res:-440.58973}  
Iteration 4:{space 3}log likelihood = {res:-440.58973}  
{res}
{txt}Probit regression{col 51}Number of obs{col 67}= {res}      1034
{txt}{col 51}LR chi2({res}9{txt}){col 67}= {res}    517.14
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-440.58973{txt}{col 51}Pseudo R2{col 67}= {res}    0.3698

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      p1_sel{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}horizon {c |}{col 14}{res}{space 2} .2710299{col 26}{space 2} .0963494{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4} .0821885{col 67}{space 3} .4598713
{txt}{space 7}other {c |}{col 14}{res}{space 2}-1.697613{col 26}{space 2}   .09049{col 37}{space 1}  -18.76{col 46}{space 3}0.000{col 54}{space 4} -1.87497{col 67}{space 3}-1.520256
{txt}{space 2}casualties {c |}{col 14}{res}{space 2} .5929668{col 26}{space 2} .0885841{col 37}{space 1}    6.69{col 46}{space 3}0.000{col 54}{space 4} .4193452{col 67}{space 3} .7665884
{txt}{space 5}set_all {c |}{col 14}{res}{space 2}-.0923239{col 26}{space 2} .0325409{col 37}{space 1}   -2.84{col 46}{space 3}0.005{col 54}{space 4}-.1561029{col 67}{space 3} -.028545
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0388361{col 26}{space 2} .0980708{col 37}{space 1}    0.40{col 46}{space 3}0.692{col 54}{space 4}-.1533791{col 67}{space 3} .2310513
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .012137{col 26}{space 2} .0039007{col 37}{space 1}    3.11{col 46}{space 3}0.002{col 54}{space 4} .0044917{col 67}{space 3} .0197822
{txt}{space 7}party {c |}{col 14}{res}{space 2}-.0280397{col 26}{space 2}  .024275{col 37}{space 1}   -1.16{col 46}{space 3}0.248{col 54}{space 4}-.0756179{col 67}{space 3} .0195384
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2}-.1264055{col 26}{space 2} .0609725{col 37}{space 1}   -2.07{col 46}{space 3}0.038{col 54}{space 4}-.2459094{col 67}{space 3}-.0069017
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2}-.1104561{col 26}{space 2} .0596506{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4}-.2273691{col 67}{space 3} .0064568
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.130349{col 26}{space 2} .3323216{col 37}{space 1}    6.41{col 46}{space 3}0.000{col 54}{space 4} 1.479011{col 67}{space 3} 2.781687
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. margins, at(set_all=(1(1)7)) 
{res}
{txt}Predictive margins{col 51}Number of obs{col 67}= {res}      1034
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(p1_sel), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:set_all}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:set_all}{space 9}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:set_all}{space 9}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:set_all}{space 9}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:set_all}{space 9}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:set_all}{space 9}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:set_all}{space 9}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .6376058{col 26}{space 2} .0202653{col 37}{space 1}   31.46{col 46}{space 3}0.000{col 54}{space 4} .5978865{col 67}{space 3} .6773251
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6159493{col 26}{space 2} .0148624{col 37}{space 1}   41.44{col 46}{space 3}0.000{col 54}{space 4} .5868196{col 67}{space 3}  .645079
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .5939365{col 26}{space 2}  .011726{col 37}{space 1}   50.65{col 46}{space 3}0.000{col 54}{space 4}  .570954{col 67}{space 3}  .616919
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5715999{col 26}{space 2} .0131109{col 37}{space 1}   43.60{col 46}{space 3}0.000{col 54}{space 4}  .545903{col 67}{space 3} .5972967
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .5489741{col 26}{space 2} .0182654{col 37}{space 1}   30.06{col 46}{space 3}0.000{col 54}{space 4} .5131745{col 67}{space 3} .5847737
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .5260967{col 26}{space 2} .0250992{col 37}{space 1}   20.96{col 46}{space 3}0.000{col 54}{space 4} .4769032{col 67}{space 3} .5752902
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .5030093{col 26}{space 2} .0326397{col 37}{space 1}   15.41{col 46}{space 3}0.000{col 54}{space 4} .4390367{col 67}{space 3} .5669819
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Plot marginal effect of set size on probability of seleting policy 1
> /// Figure 6 in main text
> marginsplot, recast(line) ciopts(recast(rline) lp(dash) color(gs9)) ///
> xtitle("Choice-Set Size") ytitle("Pr(Select PolicyOne)") title("") graphregion(color(white))

{text}{p 2 6 2}Variables that uniquely identify margins: set_all{p_end}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/admin/Dropbox/TAMU/Diss./Theory/Choice_set/RnR/Dataverse/FPA_main.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 1 Jan 2021, 23:43:50
{txt}{.-}
{smcl}
{txt}{sf}{ul off}